Every vendor selling software in 2024 claims to be “AI-powered” — which means the term now tells you almost nothing about what you’re actually buying.
For IT directors doing vendor due diligence, this is a genuine operational problem. You’re being asked to approve budget, negotiate contracts, and stake your infrastructure on systems where the AI claims range from “we run a spell-checker” to “we have a purpose-built inference pipeline running fine-tuned models on your data.” Those are not the same thing. Not even close.
What follows is a working framework for cutting through that noise. Not a buyer’s guide. A test.
The Six-Level AI Implementation Spectrum
Not all ai powered software is built the same way, and the gap between Level 1 and Level 6 is wide enough to drive a compliance violation through. Understanding where a vendor actually sits on this spectrum is the first move in any serious evaluation.
Level 1 — Rule-based automation. Logic trees, keyword matching, conditional workflows. Vendors call this AI. It is not. It’s scripting with better marketing.
Level 2 — Pre-built ML API calls. The vendor calls OpenAI or a similar provider at runtime. They own none of the model, none of the training, and often none of the data pipeline. When the API changes, their product changes. When the API goes down, their product goes down.
Level 3 — Fine-tuned third-party models. The vendor has taken a foundation model and adapted it on domain-specific data. There’s real ML work here, but the base model dependency remains. Understand who owns the fine-tuning artifacts and what happens at contract termination.
Level 4 — Proprietary model training. The vendor trains models from scratch or maintains significant architectural control. This is expensive and rare. When a vendor claims this, ask for specifics: what architecture, what training data, what evaluation benchmarks.
Level 5 — Embedded ML in core product logic. AI isn’t a feature — it’s structural to how the product operates. Predictions, classifications, and recommendations are woven into every significant user action. Removing the ML would mean rebuilding the product.
Level 6 — Adaptive systems with feedback loops. The model improves based on production data, with governance controls over how and when that learning occurs. This is where genuine enterprise ai powered software lives — and where most vendors stop talking in specifics.
The Six-Question Test Battery
Once you’ve placed a vendor on the spectrum, you need to stress-test their claims. These six questions are designed to produce either clear answers or telling hesitation.
1. Where does inference happen? On their servers, on a third-party cloud, or inside your environment? The answer determines your data exposure surface immediately. A vendor who can’t answer this precisely hasn’t thought about your security posture.
2. What data trains the model, and is your data used to train for other customers? Multi-tenant model training is a live compliance issue in healthcare, financial services, and government. “We anonymize everything” is not an acceptable answer to a data governance officer.
3. What happens to AI functionality if you terminate the contract? If the answer involves data export in formats that can’t be reused, or model weights that stay with the vendor, you don’t own what you think you own.
4. Can you audit model decisions? Explainability isn’t optional in regulated environments. If a vendor’s AI makes a credit decision, a triage recommendation, or a content moderation call, you need a decision trail. “The model said so” will not satisfy your auditors.
5. How is model drift detected and addressed? Models degrade. The question isn’t whether it happens — it’s whether the vendor has a systematic process for catching it before it causes production problems you don’t know about.
6. What’s the fallback when the AI component fails? Resilient ai powered software degrades gracefully. Brittle implementations take down core workflows when a model endpoint becomes unavailable. Ask for the architecture diagram, not the verbal answer.
Deployment Architecture Is the Enterprise Buyer’s Real Test
Marketing language lives in slide decks. Deployment architecture lives in your environment, your risk posture, and your audit findings. It’s the part of the evaluation most buyers skip — and the part that determines whether a deployment succeeds or becomes a remediation project.
The core question is simple: does this system run where your data governance policies require it to run? For many enterprise environments — particularly in financial services, healthcare, and defense-adjacent sectors — that means on-premises or in a private cloud with no outbound model calls to third-party APIs during inference.
Most vendors cannot meet this requirement. They’ve built on top of shared cloud infrastructure because it’s cheaper and faster to ship. That’s a legitimate product decision, but it’s your problem when it conflicts with your data residency obligations or your CISO’s approved architecture.
This is precisely where platforms like Peridot are built differently. Peridot runs AI inside your own infrastructure — the model execution, the data pipeline, the access controls — without routing sensitive data through external endpoints. For IT directors who’ve spent time explaining cloud vendor data handling to a compliance committee, the architectural difference is immediately legible.
The deployment question also surfaces integration reality. A vendor demo runs against clean, structured, vendor-controlled data. Your environment has legacy systems, inconsistent schemas, and access controls that were designed before AI was a procurement category. Ask the vendor to describe a real integration with systems similar to yours. The specificity of their answer tells you whether they’ve actually done it.
What Genuine Enterprise AI Infrastructure Looks Like
Genuine enterprise ai powered software has four properties that hold up under scrutiny: data isolation, access control integration, audit logging at the inference layer, and defined model governance processes.
Data isolation means the system doesn’t commingle your data with other customers’ data at any layer of the stack — storage, processing, or model training. Access control integration means the AI system respects the same identity and permissions model as the rest of your environment. Audit logging at the inference layer means every significant model call is recorded in a way that supports compliance review. Model governance means there’s a defined process for versioning, testing, and approving model changes before they hit production.
Peridot is designed around these properties from the infrastructure level up — not bolted on as compliance theater after the fact. That’s the difference between a platform built for enterprise operators and a consumer product that’s been repackaged for enterprise sales.
When a vendor can’t walk you through all four properties with technical specifics, you’re looking at either a Level 1 or Level 2 implementation dressed up as something more serious — or a product genuinely not built for the environment you’re responsible for securing.
The AI label is cheap. The infrastructure decisions underneath it are permanent. Buy accordingly.